Institute of Botany, University of Liège, Liège, Belgium.
PLoS One. 2012;7(3):e32586. doi: 10.1371/journal.pone.0032586. Epub 2012 Mar 2.
The objective of this study was to evaluate the performance of stacked species distribution models in predicting the alpha and gamma species diversity patterns of two important plant clades along elevation in the Andes. We modelled the distribution of the species in the Anthurium genus (53 species) and the Bromeliaceae family (89 species) using six modelling techniques. We combined all of the predictions for the same species in ensemble models based on two different criteria: the average of the rescaled predictions by all techniques and the average of the best techniques. The rescaled predictions were then reclassified into binary predictions (presence/absence). By stacking either the original predictions or binary predictions for both ensemble procedures, we obtained four different species richness models per taxa. The gamma and alpha diversity per elevation band (500 m) was also computed. To evaluate the prediction abilities for the four predictions of species richness and gamma diversity, the models were compared with the real data along an elevation gradient that was independently compiled by specialists. Finally, we also tested whether our richness models performed better than a null model of altitudinal changes of diversity based on the literature. Stacking of the ensemble prediction of the individual species models generated richness models that proved to be well correlated with the observed alpha diversity richness patterns along elevation and with the gamma diversity derived from the literature. Overall, these models tend to overpredict species richness. The use of the ensemble predictions from the species models built with different techniques seems very promising for modelling of species assemblages. Stacking of the binary models reduced the over-prediction, although more research is needed. The randomisation test proved to be a promising method for testing the performance of the stacked models, but other implementations may still be developed.
本研究旨在评估堆叠物种分布模型在预测安第斯山脉两个重要植物类群沿海拔高度的 alpha 和 gamma 物种多样性模式方面的性能。我们使用六种建模技术对 Anthurium 属(53 种)和凤梨科(89 种)的物种分布进行建模。我们基于两个不同的标准,在基于两个不同标准的集合模型中合并了所有同一物种的预测:所有技术的重缩放预测的平均值和最佳技术的平均值。然后,将重缩放的预测重新分类为二进制预测(存在/不存在)。通过堆叠两个集合过程的原始预测或二进制预测,我们获得了每个分类单元的四种不同的物种丰富度模型。还计算了每个海拔带(500 米)的 gamma 和 alpha 多样性。为了评估对物种丰富度和 gamma 多样性的四种预测的预测能力,我们将模型与由专家独立汇编的沿海拔梯度的真实数据进行了比较。最后,我们还测试了我们的丰富度模型是否比基于文献的多样性海拔变化的空模型表现更好。对个体物种模型的集合预测进行堆叠生成了丰富度模型,这些模型被证明与沿海拔高度观察到的 alpha 多样性丰富度模式以及从文献中得出的 gamma 多样性高度相关。总体而言,这些模型往往会过度预测物种丰富度。使用不同技术构建的物种模型的集合预测似乎非常有前途,可以用于物种组合的建模。尽管需要更多的研究,但对二进制模型的堆叠可以减少过度预测。随机化检验被证明是测试堆叠模型性能的一种很有前途的方法,但可能仍在开发其他实现方法。